A Study on Mushroom Pest and Diseases Analysis System Implementation based on Convolutional Neural Networks for Smart Farm

Kim, Kyung-Jong; Jung, Se-Hoon; So, Won-Ho; Sim, Chun-Bo

doi:10.14257/ijca.2017.10.11.06

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Honghao Gao . has various attributes [11], [12]. Due to different link lengths and uneven load capacity, it is easy to cause data loss and excessive average turnaround time during data sharing [13].…”

Section: Introductionmentioning

confidence: 99%

Stream of Unbalanced Medical Big Data Using Convolutional Neural Network

2020

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In order to address the problem that the traditional algorithm can not predict the network link load effectively, which leads to high packet loss and energy loss, long turnaround time, slow stream rate and poor anti-attack ability, the paper proposes the stream algorithm of unbalanced medical big data based on convolutional neural network (CNN). The proposed algorithm included two stages:In the first stage, the decomposition-prediction model was constructed, the combined wavelet analysis and neural network analysis were used to complete the network link load prediction; In the second stage, based on the network link load situation, we analyzed the structure of each layer of convolution neural network, constructed the medical big data stream optimization model, introduced the ReLu function to calculate the convolution neural network, solved the optimization model, and completed the stream processing of unbalanced medical big data. The experimental results show that the network link load prediction accuracy of the proposed stream algorithm is as high as 93%, the lowest packet loss rate is only 2.0%, the energy loss of the stream process is low, the rate is fast, and the anti-attack efficiency is high, which is more conducive to the realization of data stream. INDEX TERMS Convolutional neural network, unbalanced medical big data, network load forecasting, ReLu function, data stream.

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Section: Introductionmentioning

confidence: 99%